Публикация решения: update vectorstore.py

vectorstore.py

@@ -1,35 +1,37 @@
 """
-RAG vector store using Qdrant and Ollama embeddings.
+RAG vector store using ChromaDB and Ollama embeddings.
 """
 
 from pathlib import Path
 from typing import List
 
-import chromadb  # kept for compatibility if needed
+import chromadb
 from langchain.embeddings.ollama import OllamaEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.schema.document import Document
-from langchain.vectorstores import Qdrant
+from langchain.vectorstores import Chroma
 
 CHROMA_DIR = "./chroma_db"
 EMBED_MODEL = "nomic-embed-text"
 
 
-def create_vectorstore(persist_directory: str = CHROMA_DIR):
+def create_vectorstore(persist_directory: str = CHROMA_DIR) -> Chroma:
-    """Create or load a Qdrant vector store.
+    """Create or load a Chroma vector store.
 
     Parameters
     ----------
-    persist_directory:
+    persist_directory : str, optional
-        Directory where the Qdrant database is stored. If it does not exist, it will be created.
+        Directory where the Chroma database is stored. If it does not exist,
+        it will be created automatically by Chroma.
     """
     embeddings = OllamaEmbeddings(model=EMBED_MODEL)
-    # Qdrant can use a local file store via `path` argument
+    # Chroma can use a local directory for persistence
-    client = Qdrant(persist_directory=persist_directory, embedding_function=embeddings)
+    client = chromadb.PersistentClient(path=persist_directory)
-    return client
+    collection = client.get_or_create_collection(name="documents", embedding_function=embeddings)
+    return Chroma(collection=collection, embedding_function=embeddings)
 
 
-def load_documents(directory: str, vectorstore) -> None:
+def load_documents(directory: str, vectorstore: Chroma) -> None:
     """Load all .txt and .md files from *directory*, chunk them and add to the vector store.
 
     The function does not return anything; it mutates the provided collection.
@@ -44,8 +46,5 @@ def load_documents(directory: str, vectorstore) -> None:
         docs.extend(text_splitter.create_documents([content], metadata={"source": str(path)}))
 
     if docs:
-        # Qdrant expects texts and metadatas lists
+        # Chroma expects a list of documents via add_documents
-        vectorstore.add_texts(
+        vectorstore.add_documents(docs)
-            texts=[doc.page_content for doc in docs],
-            metadatas=[doc.metadata for doc in docs],
-        )